Work for hire is a completely different situation. You have to accept the terms of whoever’s paying you to do the work.
This comic is more about someone doing something as a hobby project, or writing their own software not under someone else’s direction, where they have full freedom to choose their own license.
Have you ever heard of the expression “When you have a hammer, every problem looks like a nail”?
I mean alledgedly ChatGPT passed the “bar-exam” in 2023. Which I find ridiculous considering my experiences with ChatGPT and the accuracy and usefulness I get out of it which isn’t that great at all
Exactly. If it passed the bar exam it’s because the correct solutions to the bar exam were in the training data.
The other side can immediately tell that somebody has made an imitation without understanding the concept.
No, they can’t. Just like people today think ChatGPT is intelligent despite it just being a fancy autocomplete. When it gets something obviously wrong they say those are “hallucinations”, but they don’t say they’re “hallucinations” when it happens to get things right, even though the process that produced those answers is identical. It’s just generating tokens that have a high likelihood of being the next word.
People are also fooled by parrots all the time. That doesn’t mean a parrot understands what it’s saying, it just means that people are prone to believe something is intelligent even if there’s nothing there.
ChatGPT refuses to tell illegal things, NSFW things, also medical advice and a bunch of other things
Sure, in theory. In practice people keep getting a way around those blocks. The reason it’s so easy to bypass them is that ChatGPT has no understanding of anything. That means it can’t be taught concepts, it has to be taught specific rules, and people can always find a loophole to exploit. Yes, after spending hundreds of millions of dollars on contractors in low-wage countries they think they’re getting better at blocking those off, but people keep finding new ways of exploiting a vulnerability.
Nintendo used to be a company that specialized in cards, although these days it’s more associated with carts. They made a very successful transition into gaming, but still make cards in Japan.
I can’t see Hasbro being as effective though. I’m sure Hasbro is just going to try to churn out shovelware that bears their IP so that they can monetize things they own.
Yeah, that’s basically the idea I was expressing.
Except, the original idea is about “Understanding Chinese”, which is a bit vague. You could argue that right now the best translation programs “understand chinese”, at least enough to translate between Chinese and English. That is, they understand the rules of Chinese when it comes to subjects, verbs, objects, adverbs, adjectives, etc.
The question is now whether they understand the concepts they’re translating.
Like, imagine the Chinese government wanted to modify the program so that it was forbidden to talk about subjects that the Chinese government considered off-limits. I don’t think any current LLM could do that, because doing that requires understanding concepts. Sure, you could ban key words, but as attempts at Chinese censorship have shown over the years, people work around word bans all the time.
That doesn’t mean that some future system won’t be able to understand concepts. It may have an LLM grafted onto it as a way to communicate with people. But, the LLM isn’t the part of the system that thinks about concepts. It’s the part of the system that generates plausible language. The concept-thinking part would be the part that did some prompt-engineering for the LLM so that the text the LLM generated matched the ideas it was trying to express.
The “learning” in a LLM is statistical information on sequences of words. There’s no learning of concepts or generalization.
And what do you think language and words are for? To transport information.
Yes, and humans used words for that and wrote it all down. Then a LLM came along, was force-fed all those words, and was able to imitate that by using big enough data sets. It’s like a parrot imitating the sound of someone’s voice. It can do it convincingly, but it has no concept of the content it’s using.
How do you learn as a human when not from words?
The words are merely the context for the learning for a human. If someone says “Don’t touch the stove, it’s hot” the important context is the stove, the pain of touching it, etc. If you feed an LLM 1000 scenarios involving the phrase “Don’t touch the stove, it’s hot”, it may be able to create unique dialogues containing those words, but it doesn’t actually understand pain or heat.
We record knowledge in books, can talk about abstract concepts
Yes, and those books are only useful for someone who has a lifetime of experience to be able to understand the concepts in the books. An LLM has no context, it can merely generate plausible books.
Think of it this way. Say there’s a culture where instead of the written word, people wrote down history by weaving fabrics. When there was a death they’d make a certain pattern, when there was a war they’d use another pattern. A new birth would be shown with yet another pattern. A good harvest is yet another one, and so-on.
Thousands of rugs from that culture are shipped to some guy in Europe, and he spends years studying them. He sees that pattern X often follows pattern Y, and that pattern Z only ever seems to appear following patterns R, S and T. After a while, he makes a fabric, and it’s shipped back to the people who originally made the weaves. They read a story of a great battle followed by lots of deaths, but surprisingly there followed great new births and years of great harvests. They figure that this stranger must understand how their system of recording events works. In reality, all it was was an imitation of the art he saw with no understanding of the meaning at all.
That’s what’s happening with LLMs, but some people are dumb enough to believe there’s intention hidden in there.
That is to force it to form models about concepts.
It can’t make models about concepts. It can only make models about what words tend to follow other words. It has no understanding of the underlying concepts.
You can see that by asking them to apply their knowledge to something they haven’t seen before
That can’t happen because they don’t have knowledge, they only have sequences of words.
For example a cat is closer related to a dog than to a tractor.
The only way ML models “understand” that is in terms of words or pixels. When they’re generating text related to cats, the words they’re generating are closer to the words related to dogs than the words related to tractors. When dealing with images, it’s the same basic idea. But, there’s no understanding there. They don’t get that cats and dogs are related.
This is fundamentally different from how human minds work, where a baby learns that cats and dogs are similar before ever having a name for either of them.
If you go to heaven, you’d probably want your grandmother to be the kindly old lady you remember. Her own grandmother might want her to be the little kid she remembered bouncing on her knee. She, herself, might want to be the 20 year old who was full of energy and beautiful. Is 20-year-old you going to curl up on 20-year-old grandma’s lap while she reads you a story?
The only way to really solve that is that heaven is basically being extremely high, so your “brain” is sending out happy chemicals and you don’t care at all about anything else.
But, “I can’t wait to get to heaven so I can be high as balls 24/7” doesn’t sound as wholesome as “I can’t wait to get to heaven so I can see grandma again”.
So, it’s like drugs. There’s no “you” left, just a pile of goo that’s eternally drugged out on happiness.
The construction workers also don’t have a “desire” (so to speak) to connect the cities. It’s just that their boss told them to do so.
But, the construction workers aren’t the ones who designed the road. They’re just building some small part of it. In the LLM case that might be like an editor who is supposed to go over the text to verify the punctuation is correct, but nothing else. But, the LLM is the author of the entire text. So, it’s not like a construction worker building some tiny section of a road, it’s like the civil engineer who designed the entire highway.
Somehow making them want to predict the next token makes them learn a bit of maths and concepts about the world
No, it doesn’t. They learn nothing. They’re simply able to generate text that looks like the text generated by people who do know math. They certainly don’t know any concepts. You can see that by how badly they fail when you ask them to do simple calculations. They quickly start generating text that looks like it contains fundamental mistakes, because they’re not actually doing math or anything, they’re just generating plausible next words.
The “intelligence”, the ability to anwer questions and do something alike “reasoning” emerges in the process.
No, there’s no intelligence, no reasoning. The can fool humans into thinking there’s intelligence there, but that’s like a scarecrow convincing a crow that there’s a human or human-like creature out in the field.
But we as humans might be machines, too
We are meat machines, but we’re meat machines that evolved to reproduce. That means a need / desire to get food, shelter, and eventually mate. Those drives hook up to the brain to enable long and short term planning to achieve those goals. We don’t generate language its own sake, but instead in pursuit of a goal. An LLM doesn’t have that. It merely generates plausible words. There’s no underlying drive. It’s more a scarecrow than a human.
Yeah, exactly. Say a your best friend is Muslim. Obviously, no Muslims are allowed in heaven. If you’re incapable of sadness in heaven, then you must no longer care about your best friend. But, if you no longer care about your best friend, do you actually care about anybody?
The annoying thing is that “I’ll go to work tomorrow” and “I’m going to go to work tomorrow” have subtly different meanings to English speakers, but good luck trying to come up with a rule to explain the difference to someone learning the language.
The spoken language has more vowel sounds than the alphabet has vowels, even when Danish has added three extra (æ, ø and å).
That’s one thing I think English at least did right. Other languages added extra letters and/or diacritics to try to capture all the variations on vowel sounds. But, in most languages there are far more vowel sounds than there are vowels. So, don’t make things unnecessarily complicated by adding extra letters. The one language that seems to do it basically right is Spanish, where there are only about 5 vowel sounds and they use the accent character not to show that a letter is pronounced differently, but just to cue you in on which syllable is accented when it might not be the one you expect. (With a few minor annoying variations, like el and él).
English’s future tense
There are various future tenses.
Future Simple / Simple Future: Will + [base form] – I’ll eat that later; or Going + [infinitive] – I’m going to eat that later.
Future Continuous: Will be + [present participle] – I’ll be eating that later.
Future Perfect: Will have + [past participle] – I’ll have eaten that later.
Future Perfect Continuous: Will have been + [present participle] – I’ll have been eating that later.
There’s also using the present continuous to talk about the future – I’m eating that tomorrow.
Also, the simple present – I eat that tomorrow.
English is flexible, but it’s also weird. There are a lot of distinctions that matter to native English speakers but that are really hard to put into rules. Like “will” vs. “going to”. They have slightly different meanings, but good luck coming up with an easy to understand rule about when to use each version.
What are some of the issues with Danish?
Do you know if that’s unique to Estonian, or also true of Finnish? AFAIK, Finnish (and Estonian) are a weird language branch in that most of Europe is Indo-European. Even distinct languages like Italian and German are more related to each-other than Finnish.
What does willpower have to do with it?
Phonetically, put: /pʊt/ vs. putt: /pʌt/
ʊ is the sound from words like “book”, “hook”, “pull” or "should.
ʌ is the sound from words like “gut”, “double”, “butter”, “luck”, etc.
The one freedom the GPL removes is the freedom to be a leech. If you’re linking to GPL code, you are agreeing to follow the same rules as everybody else who has contributed to that code. Nobody gets a pass